# hdbscan **Repository Path**: mirrors_lepy/hdbscan ## Basic Information - **Project Name**: hdbscan - **Description**: A high performance implementation of HDBSCAN clustering. - **Primary Language**: Unknown - **License**: BSD-3-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-09-25 - **Last Updated**: 2026-09-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README .. image:: https://img.shields.io/pypi/v/hdbscan.svg :target: https://pypi.python.org/pypi/hdbscan/ :alt: Version .. image:: https://img.shields.io/pypi/l/hdbscan.svg :target: https://github.com/nicodv/hdbscan/blob/master/LICENSE :alt: License ======= HDBSCAN ======= HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). Based on the paper: R. Campello, D. Moulavi, and J. Sander, *Density-Based Clustering Based on Hierarchical Density Estimates* In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013 Notebooks `comparing HDBSCAN to other clustering algorithms `_, explaining `how HDBSCAN works `_ and `comparing performance with other python clustering implementations `_ are available. ------------------ How to use HDBSCAN ------------------ The hdbscan package inherits from sklearn classes, and thus drops in neatly next to other sklearn clusterers with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe, or sparse matrix) of shape ``(num_samples x num_features)``; an array (or sparse matrix) giving a distance matrix between samples. .. code:: python import hdbscan clusterer = hdbscan.HDBSCAN(min_cluster_size=10) cluster_labels = clusterer.fit_predict(data) ----------- Performance ----------- Significant effort has been put into making the hdbscan implementation as fast as possible. It is `orders of magnitude faster than the reference implementation `_ in Java, and is currently faster than highly optimized single linkage implementations in C and C++. `version 0.7 performance can be seen in this notebook `_ . In particular `performance on low dimensional data is better than sklearn's DBSCAN `_ , and via support for caching with joblib, re-clustering with different parameters can be almost free. ------------------------ Additional functionality ------------------------ The hdbscan package comes equipped with visualization tools to help you understand your clustering results. After fitting data the clusterer object has attributes for: * The condensed cluster hierarchy * The robust single linkage cluster hierarchy * The reachability distance minimal spanning tree All of which come equipped with methods for plotting and converting to Pandas or NetworkX for further analysis. See the notebook on `how HDBSCAN works `_ for examples and further details. The clusterer objects also have an attribute providing cluster membership strengths, resulting in optional soft clustering (and no further compute expense). Finally each cluster also receives a persistence score giving the stability of the cluster over the range of distance scales present in the data. This provides a measure of the relative strength of clusters. ----------------- Outlier Detection ----------------- The HDBSCAN clusterer objects also support the GLOSH outlier detection algorithm. After fitting the clusterer to data the outlier scores can be accessed via the ``outlier_scores_`` attribute. The result is a vector of score values, one for each data point that was fit. Higher scores represent more outlier like objects. Selecting outliers via upper quantiles is often a good approach. Based on the paper: R.J.G.B. Campello, D. Moulavi, A. Zimek and J. Sander *Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection*, ACM Trans. on Knowledge Discovery from Data, Vol 10, 1 (July 2015), 1-51. --------------------- Robust single linkage --------------------- The hdbscan package also provides support for the *robust single linkage* clustering algorithm of Chaudhuri and Dasgupta. As with the HDBSCAN implementation this is a high performance version of the algorithm outperforming scipy's standard single linkage implementation. The robust single linkage hierarchy is available as an attribute of the robust single linkage clusterer, again with the ability to plot or export the hierarchy, and to extract flat clusterings at a given cut level and gamma value. Example usage: .. code:: python import hdbscan clusterer = hdbscan.RobustSingleLinkage(cut=0.125, k=7) cluster_labels = clusterer.fit_predict(data) hierarchy = clusterer.cluster_hierarchy_ alt_labels = hierarchy.get_clusters(0.100, 5) hierarchy.plot() Based on the paper: K. Chaudhuri and S. Dasgupta. *"Rates of convergence for the cluster tree."* In Advances in Neural Information Processing Systems, 2010. ---------- Installing ---------- Fast install, presuming you have sklearn and all its requirements installed: .. code:: bash pip install hdbscan If pip is having difficulties pulling the dependencies then we'd suggest installing the dependencies manually using anaconda followed by pulling hdbscan from pip: .. code:: bash conda install cython conda install scikit-learn pip install hdbscan For a manual install get this package: .. code:: bash wget https://github.com/lmcinnes/hdbscan/archive/master.zip unzip master.zip rm master.zip cd hdbscan-master Install the requirements .. code:: bash sudo pip install -r requirements.txt or .. code:: bash conda install scikit-learn cython Install the package .. code:: bash python setup.py install Coming soon: installing via conda. --------- Licensing --------- The hdbscan package is 3-clause BSD licensed. Enjoy.